






"""simple docstring"""
import unittest
from typing import Dict, List, Optional, Union

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import BridgeTowerImageProcessor



class        UpperCamelCase__ (				unittest.TestCase):







    """simple docstring"""







    def __init__(		self :	Any				,    UpperCamelCase_ :	Any				,    UpperCamelCase_ :	bool = True				,    UpperCamelCase_ :	Dict[str, int] = None				,    UpperCamelCase_ :	int = 3_2				,    UpperCamelCase_ :	bool = True				,    UpperCamelCase_ :	Union[int, float] = 1 / 2_5_5				,    UpperCamelCase_ :	bool = True				,    UpperCamelCase_ :	bool = True				,    UpperCamelCase_ :	Optional[Union[float, List[float]]] = [0.48_145_466, 0.4_578_275, 0.40_821_073]				,    UpperCamelCase_ :	Optional[Union[float, List[float]]] = [0.26_862_954, 0.26_130_258, 0.27_577_711]				,    UpperCamelCase_ :	bool = True				,    UpperCamelCase_ :	Optional[Any]=7				,    UpperCamelCase_ :	List[str]=3_0				,    UpperCamelCase_ :	Any=4_0_0				,    UpperCamelCase_ :	Tuple=3				,    ):





           '''simple docstring'''

           __magic_name__      				=    parent
           __magic_name__      				=    do_resize
           __magic_name__      				=    size if size is not None else {'shortest_edge': 2_8_8}
           __magic_name__      				=    size_divisor
           __magic_name__      				=    do_rescale
           __magic_name__      				=    rescale_factor
           __magic_name__      				=    do_normalize
           __magic_name__      				=    do_center_crop
           __magic_name__      				=    image_mean
           __magic_name__      				=    image_std
           __magic_name__      				=    do_pad
           __magic_name__      				=    batch_size
           __magic_name__      				=    num_channels
           __magic_name__      				=    min_resolution
           __magic_name__      				=    max_resolution







    def 				a__			(		self :	Union[str, Any] ):





           '''simple docstring'''

           return {
               "image_mean": self.image_mean,
               "image_std": self.image_std,
               "do_normalize": self.do_normalize,
               "do_resize": self.do_resize,
               "size": self.size,
               "size_divisor": self.size_divisor,
           }







    def 				a__			(		self :	Optional[Any]				,    UpperCamelCase_ :	List[Any]				,    UpperCamelCase_ :	Tuple=False ):





           '''simple docstring'''

           if not batched:
                  __magic_name__      				=    self.size['shortest_edge']
                  __magic_name__      				=    image_inputs[0]
                  if isinstance(UpperCamelCase_				,    Image.Image ):
                         __magic_name__				,  __magic_name__      				=    image.size
                  else:
                         __magic_name__				,  __magic_name__      				=    image.shape[1], image.shape[2]
                  __magic_name__      				=    size / min(UpperCamelCase_				,    UpperCamelCase_ )
                  if h < w:
                         __magic_name__				,  __magic_name__      				=    size, scale * w
                  else:
                         __magic_name__				,  __magic_name__      				=    scale * h, size

                  __magic_name__      				=    int((1_3_3_3 / 8_0_0) * size )
                  if max(UpperCamelCase_				,    UpperCamelCase_ ) > max_size:
                         __magic_name__      				=    max_size / max(UpperCamelCase_				,    UpperCamelCase_ )
                         __magic_name__      				=    newh * scale
                         __magic_name__      				=    neww * scale

                  __magic_name__				,  __magic_name__      				=    int(newh + 0.5 ), int(neww + 0.5 )
                  __magic_name__				,  __magic_name__      				=    (
                      newh // self.size_divisor * self.size_divisor,
                      neww // self.size_divisor * self.size_divisor,
                  )

           else:
                  __magic_name__      				=    []
                  for image in image_inputs:
                         __magic_name__				,  __magic_name__      				=    self.get_expected_values([image] )
                         expected_values.append((expected_height, expected_width) )
                  __magic_name__      				=    max(UpperCamelCase_				,    key=lambda UpperCamelCase_ : item[0] )[0]
                  __magic_name__      				=    max(UpperCamelCase_				,    key=lambda UpperCamelCase_ : item[1] )[1]

           return expected_height, expected_width



@require_torch
@require_vision
class        UpperCamelCase__ (				a_							,	unittest.TestCase):







    """simple docstring"""
    __UpperCAmelCase           = BridgeTowerImageProcessor if is_vision_available() else None







    def 				a__			(		self :	Any ):





           '''simple docstring'''

           __magic_name__      				=    BridgeTowerImageProcessingTester(self )







    @property
    def 				a__			(		self :	Any ):





           '''simple docstring'''

           return self.image_processor_tester.prepare_image_processor_dict()







    def 				a__			(		self :	Dict ):





           '''simple docstring'''

           __magic_name__      				=    self.image_processing_class(**self.image_processor_dict )
           self.assertTrue(hasattr(UpperCamelCase_				,    'image_mean' ) )
           self.assertTrue(hasattr(UpperCamelCase_				,    'image_std' ) )
           self.assertTrue(hasattr(UpperCamelCase_				,    'do_normalize' ) )
           self.assertTrue(hasattr(UpperCamelCase_				,    'do_resize' ) )
           self.assertTrue(hasattr(UpperCamelCase_				,    'size' ) )
           self.assertTrue(hasattr(UpperCamelCase_				,    'size_divisor' ) )







    def 				a__			(		self :	Optional[Any] ):





           '''simple docstring'''

           pass







    def 				a__			(		self :	Optional[Any] ):





           '''simple docstring'''

           __magic_name__      				=    self.image_processing_class(**self.image_processor_dict )
           # create random PIL images
           __magic_name__      				=    prepare_image_inputs(self.image_processor_tester				,    equal_resolution=UpperCamelCase_ )
           for image in image_inputs:
                  self.assertIsInstance(UpperCamelCase_				,    Image.Image )

           # Test not batched input
           __magic_name__      				=    image_processing(image_inputs[0]				,    return_tensors='pt' ).pixel_values

           __magic_name__				,  __magic_name__      				=    self.image_processor_tester.get_expected_values(UpperCamelCase_ )
           self.assertEqual(
               encoded_images.shape				,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)				,    )

           # Test batched
           __magic_name__      				=    image_processing(UpperCamelCase_				,    return_tensors='pt' ).pixel_values

           __magic_name__				,  __magic_name__      				=    self.image_processor_tester.get_expected_values(UpperCamelCase_				,    batched=UpperCamelCase_ )
           self.assertEqual(
               encoded_images.shape				,    (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               )				,    )







    def 				a__			(		self :	str ):





           '''simple docstring'''

           __magic_name__      				=    self.image_processing_class(**self.image_processor_dict )
           # create random numpy tensors
           __magic_name__      				=    prepare_image_inputs(self.image_processor_tester				,    equal_resolution=UpperCamelCase_				,    numpify=UpperCamelCase_ )
           for image in image_inputs:
                  self.assertIsInstance(UpperCamelCase_				,    np.ndarray )

           # Test not batched input
           __magic_name__      				=    image_processing(image_inputs[0]				,    return_tensors='pt' ).pixel_values

           __magic_name__				,  __magic_name__      				=    self.image_processor_tester.get_expected_values(UpperCamelCase_ )
           self.assertEqual(
               encoded_images.shape				,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)				,    )

           # Test batched
           __magic_name__      				=    image_processing(UpperCamelCase_				,    return_tensors='pt' ).pixel_values

           __magic_name__				,  __magic_name__      				=    self.image_processor_tester.get_expected_values(UpperCamelCase_				,    batched=UpperCamelCase_ )
           self.assertEqual(
               encoded_images.shape				,    (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               )				,    )







    def 				a__			(		self :	List[str] ):





           '''simple docstring'''

           __magic_name__      				=    self.image_processing_class(**self.image_processor_dict )
           # create random PyTorch tensors
           __magic_name__      				=    prepare_image_inputs(self.image_processor_tester				,    equal_resolution=UpperCamelCase_				,    torchify=UpperCamelCase_ )
           for image in image_inputs:
                  self.assertIsInstance(UpperCamelCase_				,    torch.Tensor )

           # Test not batched input
           __magic_name__      				=    image_processing(image_inputs[0]				,    return_tensors='pt' ).pixel_values

           __magic_name__				,  __magic_name__      				=    self.image_processor_tester.get_expected_values(UpperCamelCase_ )
           self.assertEqual(
               encoded_images.shape				,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)				,    )

           # Test batched
           __magic_name__      				=    image_processing(UpperCamelCase_				,    return_tensors='pt' ).pixel_values

           __magic_name__				,  __magic_name__      				=    self.image_processor_tester.get_expected_values(UpperCamelCase_				,    batched=UpperCamelCase_ )
           self.assertEqual(
               encoded_images.shape				,    (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               )				,    )







"""simple docstring"""
def 							A						(				__snake_case: int = 3 ,     __snake_case: int = 7 ,     __snake_case: int = 1_0_0_0_0_0_0  )    ->		int:

       """simple docstring"""





       __magic_name__      				=    0
       __magic_name__      				=    1

       for current_denominator in range(1 ,     limit + 1  ):
              __magic_name__      				=    current_denominator * numerator // denominator
              if current_denominator % denominator == 0:
                     current_numerator -= 1
              if current_numerator * max_denominator > current_denominator * max_numerator:
                     __magic_name__      				=    current_numerator
                     __magic_name__      				=    current_denominator
       return max_numerator


if __name__ == "__main__":
 print(solution(numerator=3, denominator=7, limit=1_0_0_0_0_0_0))